code: def pyro_sample_saas_lengthscales(
      dim: int,
      alpha: float = 0.1,
      **tkwargs: Any,
  ) -> Tensor:
      tausq = pyro.sample(
          "kernel_tausq",
          # pyre-fixme[16]: Module `distributions` has no attribute `HalfCauchy`.
          pyro.distributions.HalfCauchy(torch.tensor(alpha, **tkwargs)),
      )
      inv_length_sq = pyro.sample(
          "_kernel_inv_length_sq",
          # pyre-fixme[16]: Module `distributions` has no attribute `HalfCauchy`.
          pyro.distributions.HalfCauchy(torch.ones(dim, **tkwargs)),
      )
      inv_length_sq = pyro.deterministic(
          "kernel_inv_length_sq", tausq * inv_length_sq
      )
      lengthscale = pyro.deterministic(
          "lengthscale",
          (1.0 / inv_length_sq).sqrt(),  # pyre-ignore [16]
      )
      return lengthscale
  
file_path: Ax/ax/models/torch/fully_bayesian_model_utils.py
imports: from typing import Any, Dict, List, Optional, Tuple
  import pyro
  import torch
  from ax.models.torch.botorch_defaults import _get_model
  from botorch.models.gp_regression import MIN_INFERRED_NOISE_LEVEL
  from botorch.models.gpytorch import GPyTorchModel
  from botorch.models.model_list_gp_regression import ModelListGP
  from gpytorch.kernels import MaternKernel, RBFKernel, ScaleKernel
  from torch import Tensor
label_desc: Use image node_id 7 for calling a global function with example usage: pyro_sample_saas_lengthscales(dim, alpha) and returns: lengthscale